To accelerate the MRI acquisition, the previous works [1]}, [2]}, [3]}, [4]} are mainly divided into parallel imaging and compressed sensing. With the renaissance of deep neural networks, deep learning have been widely used for MRI reconstruction and have achieved remarkable performance. Wang  [5]} first proposed a multi-layer CNN model for MRI reconstruction and Jin  [6]} adopted Unet [7]} to recover a clear MR image. To further improve the performance of MRI reconstruction, the cross-domain [8]}, [9]}, [10]}, [11]} were proposed. However, these methods separately processed the undersampled MRI in the Spatial and Frequency domains and cannot learn the local and global information, simultaneously. In contrast, to learn the global information, Wu  [12]} proposed a self-attention network for MR imaging and Feng  [13]} designed a transformer network for MR imaging. However, the self-attention [14]}, [15]}, [16]} and transformer [17]}, [18]} have high computation complexity and occupy a huge number of GPU memory. Moreover, the transformer [18]}, [20]} is difficult to optimize and requires a large-scale training dataset. Different from them, in this work, the proposed Spatial and Fourier Layer (SFL) can simultaneously learn the local and global information of the feature, while the required memory and the computation complexity of SFL based on the Fast Fourier Transform are similar to the convolution layer.
